ESTIMATION OF A SEMIPARAMETRIC TRANSFORMATION MODEL IN THE PRESENCE OF ENDOGENEITY
Anne Vanhems, Ingrid Van Keilegom
Abstract
Open-access reader
Anne Vanhems, Ingrid Van Keilegom
Abstract
Open-access reader
We consider a semiparametric transformation model, in which the regression function has an additive nonparametric structure and the transformation of the response is assumed to belong to some parametric family. We suppose that endogeneity is present in the explanatory variables. Using a control function approach, we show that the proposed model is identified under suitable assumptions, and propose a profile estimation method for the transformation. The proposed estimator is shown to be asymptotically normal under certain regularity conditions. A simulation study shows that the estimator behaves well in practice. Finally, we give an empirical example using the U.K. Family Expenditure Survey.
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We consider a semiparametric transformation model, in which the regression function has an additive nonparametric structure and the transformation of the response is assumed to belong to some parametric family. We suppose that endogeneity is present in the explanatory variables. Using a control function approach, we show that the proposed model is identified under suitable assumptions, and propose a profile estimation method for the transformation. The proposed estimator is shown to be asymptotically normal under certain regularity conditions. A simulation study shows that the estimator behaves well in practice. Finally, we give an empirical example using the U.K. Family Expenditure Survey.
Key concepts: Endogeneity, Semiparametric regression, Mathematics, Estimator, Semiparametric model, Econometrics, Transformation (genetics), Nonparametric regression